Ruiz Pujadas, E.; Díaz-Caneja, C.M.; Stevanovic, D.; Ferrer Quintero, M.; Martín-Isla, C.; Hernández-González, J.; Atehortúa, A.; Lazrak, N.; Pries, L.; Delespaul, P.; Camacho, M.; Gülöksüz, S.; Rutten, B.P.F.; Lekadir, K. "Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning." Cognitive Computation 17 (2025): 152-. Dang, V.N.; Cecil, C.; Pariante, C.M.; Hernández-González, J.; Lekadir, K. "Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction." PLOS Digital Health 4 (2025): e0000982-. Dang, V.N.; Campello, V.M.; Hernández-González, J.; Lekadir, K. "Empirical comparison of post-processing debiasing methods for machine learning classifiers in healthcare." Journal of Healthcare Informatics Research undef (2025): undef-. Serrano-López, F.; Ger-Roca, S.; Salamó, M.; Hernández-González, J. "Modeling river flow for flood forecasting: A case study on the Ter river." Applied Computing and Geosciences 23 (2024): 100181-. Dang, V.N.; Cascarano, A.; Mulder, R.H.; Cecil, C.; Zuluaga, M.A.; Hernández-González, J.; Lekadir, K. "Fairness and bias correction in machine learning for depression prediction across four study populations." Scientific Reports 14 (2024): -. Hernández-González, J.; Herrara, P.J. "On the supervision of peer assessment tasks: an efficient instructor guidance technique." IEEE Transactions on Learning Technologies 16 (2023): 926-939. Villar, J.; González-Martín, J.M.; Hernández-González, J.; Armengol, M.A.; Fernández, C.; Martín-Rodríguez, C.; Mosteiro, F.; Martínez, D.; Sánchez-Ballesteros, J.; Ferrando, C.; Domínguez-Berrot, A.M.; Añón, J.M.; Parra, L.; "Predicting ICU Mortality in Acute Respiratory Distress Syndrome Patients Using Machine Learning: The Predicting Outcome and STratifiCation of severity in ARDS (POSTCARDS) Study." Critical Care Medicine 51 (2023): 1638-1649. Cascarano, A.; Mur-Petit, J.; Hernández-González, J.; Camacho, M.;de Toro Eadie, N.; Gkontra, P.; Chadeau-Hyam, M.; Vitrià, J.; Lekadir, K. "Machine and deep learning for longitudinal biomedical data: a review of methods and applications." Artificial Intelligence Review 56 (2023): 1711-1771. Hernández-González, J.; Pérez, A. "On the relative value of weak information of supervision for learning generative models: An empirical study." International Journal of Approximate Reasoning 150 (2022): 258-272. Hernandez-Gonzalez, J.; Valls, O.; Torres-Martín, A.; Cerquides, J. "Modeling three sources of uncertainty in assisted reproductive technologies with probabilistic graphical models." Computers in Biology and Medicine 150 (2022): 106160-. Beñaran-Muñoz, I.; Hernández-González, J.; Pérez, A. "On the use of the descriptive variable for enhancing the aggregation of crowdsourced labels." Knowledge and Information Systems (2022): -. Benaran-Munoz, I.; Hernandez-Gonzalez, J.; Perez, A. "Machine learning from crowds using candidate set-based labelling." IEEE Intelligent Systems (2022): -. Cerquides, J.; Mülâyim, M.O.; Hernández-González, J.; Shankar, A.R.; Fernandez-Marquez, J.L. "A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data." Mathematics 9 (2021): 875-. Hernández-González, J.; Cerquides, J. "A Robust Solution to Variational Importance Sampling of Minimum Variance." Entropy 22 (2020): 1405-. Hernández-González, J.; Inza, I.; Granado, I.; Basurko, O.C.; Fernandes, J.A.; Lozano, J.A. "Aggregated outputs by linear models: an application on marine litter beaching prediction." Information Sciences 481 (2019): 381-393. Hernández-González, J. "A framework for evaluation in learning from label proportions." Progress in Artificial Intelligence 8 (2019): 359-373. Granado, I.; Basurko, O.C.; Rubio, A.; Ferrer, L.; Hernández-González, J.; Epelde, I.; Fernandes, J.A. "Beach litter forecasting on the south-eastern coast of the Bay of Biscay: a bayesian networks approach." Continental Shelf Research 180 (2019): 14-23. Hernández-González, J.; Inza, I.; Lozano, J.A. "A note on the behavior of majority voting in multi-class domains with biased annotators." IEEE Transactions on Knowledge and Data Engineering 31 (2018): 195-200. Hernández-González, J.; Rodriguez, D.; Inza, I.; Harrison, R.; Lozano, J.A. "Two datasets of defect reports labeled by a crowd of annotators of unknown reliability." Data in Brief 18 (2018): 840-845. Hernández-González, J; Rodriguez, D.; Inza, I., Harrison, R.; Lozano, J.A. "Learning to classify software defects from crowds: a novel approach." Applied Soft Computing 62 (2017): 579-591. Hernández-González, J.; Inza, I.; Lozano, J.A. "Learning from proportions of positive and unlabeled examples." International Journal of Intelligent Systems 32 (2016): 109-133. Hernández-González, J.; Inza, I.; Crisol-Ortíz, L.; Guembe, M.A.; Iñarra, M.J.; Lozano, J.A. "Fitting the data from embryo implantation prediction: learning from label proportions." Statistical Methods in Medical Research 27 (2016): 1056-1066. Hernández-González, J.; Inza, I.; Lozano, J.A. "Weak supervision and other non-standard classification problems: a taxonomy." Pattern Recognition Letters 69 (2015): 49-55. Hernández-González, J.; Inza, I.; Lozano, J.A. "Multidimensional learning from crowds: usefulness and application of expertise detection." International Journal of Intelligent Systems 30 (2015): 326-354. Hernández-González, J.; Inza, I.; Lozano, J.A. "Learning Bayesian network classifiers from label proportions." Pattern Recognition 46 (2013): 3425-3440.